Method and system for topic disambiguation and classification

ABSTRACT

A method for generating recommendations involves selecting a first platform message, making a first determination that the first platform message is potentially associated with a plurality of topics including a first topic and a second topic, obtaining additional information associated with the first platform message including at least one of information about an account that authored the first platform message and information about third party accounts engaging with the first platform message, making a second determining that the first platform message is associated with the first topic using the plurality of topics and at least a portion of the additional information, wherein the first topic is an initial classification of the first platform message, generating a recommendation for at least one account based on the second determination, and providing the recommendation to at least one account.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/909,544, filed on Jun. 23, 2020, which is a continuation of U.S.application Ser. No. 16/167,448, filed on Oct. 22, 2018, U.S. Pat. No.10,701,175, issued Jun. 30, 2020, which is a continuation of U.S.application Ser. No. 14/836,407, filed on Aug. 26, 2015, U.S. Pat. No.10,108,674, issued Oct. 23, 2018, which claims the benefit of U.S.Provisional Application No. 62/041,827 filed on Aug. 26, 2014, thecontents of which are incorporated by reference herein.

BACKGROUND

Users of social networks typically author and distribute content via thesocial network. Traditional mechanisms for ascertaining the nature ofthe content are very limited. If the social network is unable toascertain the nature of the content, then the social network's abilityto provide additional relevant services based on the nature of thecontent is limited.

SUMMARY

In general, in one aspect, the invention relates to a method forgenerating recommendations, comprising selecting a first platformmessage, making a first determination that the first platform message ispotentially associated with a plurality of topics comprising a firsttopic and a second topic, obtaining additional information associatedwith the first platform message, wherein the additional informationcomprises at least one selected from a group consisting of informationabout an account that authored the first platform message andinformation about third party accounts engaging with the first platformmessage, making a second determining that the first platform message isassociated with the first topic using the plurality of topics and atleast a portion of the additional information, wherein the first topicis an initial classification of the first platform message, generating arecommendation for at least one account based on the seconddetermination, and providing the recommendation to at least one account.

In general, in one aspect, the invention relates to a method forsearching platform messages comprising selecting a first platformmessage, making a first determination that the first platform message inassociated with a first topic and a second topic, obtaining additionalinformation associated with the platform message, wherein the additionalinformation comprises at least one selected from a group consisting ofinformation about an account that authored the first platform messageand information about third party accounts engaging with the firstplatform message, making a second determining that the first platformmessage is associated with the first topic using the plurality of topicsand at least a portion of the additional information, based on thesecond determination, associating the first platform message with amessage search pool, after associating the first platform message withthe message search pool, receiving a search query associated with thefirst topic, and providing, in response to the search query, the firstplatform message.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 shows a system in accordance with one or more embodiments of theinvention.

FIG. 2 shows a flow chart for multi-modal classification of a message inaccordance with one or more embodiments of the invention.

FIG. 3 shows a flowchart for classifying the text of a message to obtaina topic classification in accordance with one or more embodiments of theinvention.

FIG. 4 shows a flow chart for classifying images of a message inaccordance with one or more embodiments of the invention.

FIG. 5 shows a computing system in accordance with one or moreembodiments of the invention.

DETAILED DESCRIPTION

Specific embodiments of the invention will now be described in detailwith reference to the accompanying figures. In the following detaileddescription of embodiments of the invention, numerous specific detailsare set forth in order to provide a more thorough understanding of theinvention. However, it will be apparent to one of ordinary skill in theart that the invention may be practiced without these specific details.In other instances, well-known features have not been described indetail to avoid unnecessarily complicating the description

In the following description of FIGS. 1-5 , any component described withregard to a figure, in various embodiments of the invention, may beequivalent to one or more like-named components described with regard toany other figure. For brevity, descriptions of these components will notbe repeated with regard to each figure. Thus, each and every embodimentof the components of each figure is incorporated by reference andassumed to be optionally present within every other figure having one ormore like-named components. Additionally, in accordance with variousembodiments of the invention, any description of the components of afigure is to be interpreted as an optional embodiment which may beimplemented in addition to, in conjunction with, or in place of theembodiments described with regard to a corresponding like-namedcomponent in any other figure.

In general, embodiments of the invention relate to a method and systemfor determining a topic associated with a given platform message. Once atopic has been associated with a platform message, one or morerecommendations for (or based on) the platform message may bedetermined. Additionally, or alternatively, once a topic has beenassociated with the platform message, the platform message may be placedin message search pool and subsequently used to service search queries.

FIG. 1 shows a system in accordance with one or more embodiments of theinvention in accordance with one or more embodiments of the invention.The system includes one or more client devices (100) and a socialnetwork platform (102). Each of these components is described below.

As shown in FIG. 1 , the social network platform (102) has multiplecomponents including an account repository (104), a topic modelsrepository (106), a message repository (108), and a classificationengine (110), as well as other components that are not shown in FIG. 1 .Various components of the social network platform (102) may be locatedon the same device (e.g., a server, mainframe, desktop Personal Computer(PC), laptop, Personal Digital Assistant (PDA), telephone, mobile phone,kiosk, cable box, and any other device) or may be located on separatedevices connected by a network (e.g., a local area network (LAN), theInternet, etc.) using any combination of wired and wireless connectionsusing any combination of communication protocols. Those skilled in theart will appreciate that there may be more than one of each separatecomponent running on a device, as well as any combination of thesecomponents within a given embodiment of the invention.

A social network platform (102) connects users to other users of thesocial network platform (102), exchanges platform messages betweenconnected users of the social network platform (102), services searchqueries from users, and/or provides an interface for a user (e.g., viaclient devices (100)) to create and view platform messages and othercontent (e.g., recommendations). In one or more embodiments of theinvention, platform messages may be broadcast (or multicast) platformmessages that are transmitted to at least a set of users. The users inthe set may be self-selected (e.g., followers of the transmitting user)or users that satisfy a certain status with report to the transmittinguser (e.g., belong to a group such as friends, family, etc.). Theplatform messages may include, but is not limited to, a comment from auser, a reference to a geographic location, personal status update, anofficial statement by a user representing an organization, a referenceto another user of the social network, one or more terms descriptive ofthe message, an offer to buy or sell goods and/or services, otherinformation not listed above or any combination thereof. Each platformmessages may include, but is not limited to, text, universal resourcelocators (URLs), pictures, media files, multimedia files, other elementsnot listed above or any combination thereof. In one or more embodimentsof the invention, the social network platform (102) may includerestrictions on the size of the platform messages, such as a restrictionon number of characters, size of included media, and other restrictionsnot listed above.

In one or more embodiments of the invention, the social network platform(102) is a platform for facilitating real-time communication between oneor more entities. For example, the social network platform (102) maystore millions of accounts of individuals, businesses, and/or otherentities (e.g., pseudonym accounts, novelty accounts, etc.). One or moreusers of each account may use the social network platform (102) to sendplatform messages to other accounts inside and/or outside of the socialnetwork platform (102). The social network platform (102) may beconfigured to enable users to communicate in “real-time”, i.e., toconverse with other users with a minimal delay and to conduct aconversation with one or more other users during concurrent (which mayinclude simultaneous) sessions. In other words, the social networkplatform (102) may allow a user to broadcast (or multicast) platformmessages and may display the platform messages to one or more otherusers within a reasonable time frame so as to facilitate a “live”conversation (or interaction) between the users. Recipients of aplatform message may have a predefined graph relationship with anaccount transmitting the platform message. In one or more embodiments ofthe invention, the user is not an account holder or is not logged in toan account of the social network platform (102). In this case, thesocial network platform (102) may be configured to allow the user totransmit platform messages and/or to utilize other functionality of thesocial network platform (102) by associating the user with a temporaryaccount or identifier.

In one or more embodiments of the invention, the social network platformincludes functionality to perform the methods shown in FIGS. 2-4 .

In one or more embodiment of the invention, a client device (100)corresponds to any computing system (as described below in FIG. 3 ) thatis configured to interface with the social network platform (102). Forexample, a client device may include a web browser application thatenables the user of the client device to interface with the socialnetwork platform. In this example, the website that the user accesses,via the web browser application, may be a website for the social networkplatform (e.g., www.twitter.com) or may be a website that includesembedded content from the social network platform but is not a websitefor the social network platform (e.g., www.cnn.com may include embeddedcontent from one or more social network platforms). In another example,the client device may have loaded thereon one or more applicationsprovided by the social network platform that enables the user of theclient device to interact with the social network platform, e.g.,Twitter iOS application. In another example, the client device may haveloaded thereon an application(s) that includes embedded content (e.g.,platform messages, recommendations) created and/or distributed by thesocial network platform (or by 3rd parties), where such applicationsaccess the content from the social network platform via APIs (or othermechanisms) provided by the social network platform.

In one or more embodiments of the invention, the client devices areconfigured to interface with the social network platform using afrontend module (not shown) associated with the social network platform.In one embodiment of the invention, the frontend module is a softwareapplication or a set of related software applications configured tocommunicate with external entities (e.g., a client device (100)). Thefrontend module may include the application programming interface (API)and/or any number of other components used for communicating withentities outside of the social network platform (102). The API mayinclude any number of specifications for making requests from and/orproviding data to the social network platform (102).

In one or more embodiments of the invention, a user may use any clientdevice (100) to receive the platform messages, recommendations(described below), receive responses to search queries, etc. Forexample, where the user uses a web-browser application executing on theclient device to access the social network platform (102), an API of thefrontend module may be utilized to define one or more streams and/or toserve the stream data to the client device for presentation to the user.

In one or more embodiments of the invention, the account repository(104) stores information about accounts in the social media platform.Specifically, the account repository (104) may store account data itemsthat include information about a location of a user (or entity thatcontrols the account), a self-description of the user (or entity thatcontrols the account), zero, one or more interest topics associated withthe account, zero, one or more expertise topics associated with theaccount. The information stored in an account repository may be providedby a user of the account, may be provided by an entity associated withthe account (e.g., information provided by a company if the companycontrols the account), may be generated in accordance with FIG. 2 ,and/or may be generated in accordance with any other mechanism. Theaccount repository may include additional or other information about theaccount without departing from the invention.

In one embodiment of the invention, an interest topic corresponds to atopic in which that one or more users of the account have expressedinterest. The interest topic associated with an account may be based ondirect input from the one or more users of the account, e.g., one ormore users of the account expressly indicate that they are interested ina topic. The interest topic associated with an account may be based onindirect input from the one or more users of the account, e.g., thesocial network platform (or a 3rd party entity) may determine, based theuser's engagement (described below), that one or more users of theaccount are likely interested in a particular topic.

In one embodiment of the invention, an expertise topic corresponds to atopic that others in the social network believe one or more users of anaccount is an expert. Said another way, unlike the interest topics foran account that are based on actions performed by the account, theexpertise topics for an account are based on actions performed by otheraccounts.

Continuing with the discussion of FIG. 1 , in one embodiment of theinvention, the topic models repository (106) includes one or more topicmodels that are used to analyze the content of a platform message (seeFIG. 2 , below) and/or determine whether there is a conflict between twoor more topics. In one embodiment of the invention, the topic models mayinclude character strings, keywords, phrases, clauses, hashtags, etc.that mapped to one or more topics. For example, if “NBA” is included inany portion of the platform message then the topic model may indicatethat there is a high likelihood that the platform message is associatedwith the topic “basketball”. In another embodiment of the invention, thetopic models may include mappings between a topic and one/or moreconflicting topics. For example, the topic model may include the topic“heavy metal” mapped to the topic of “classical music”, which indicatesthat there is a conflict between the topics of heavy metal and classicalmusic. Said another way, there is a low likelihood that a singleplatform message could be associated with both of the aforementionedtopics. The topic models may be manually generated by users (oradministrators) of the social network platform, automatically generatedby the social network platform, and/or obtained from 3rd parties thatare not users or administrators of the social network platform.

In one or more embodiments of the invention, the social network platformmay include a message repository (108), where the message repositoryincludes functionality to store message data items that include platformmessages and platform messages metadata. The platform messages metadatamay include, but is not limited to, an identifier of the originatingaccount of the platform message, a list of accounts who received theplatform message, a number of accounts who received the platformmessage, statistics (e.g., a ratio of connected accounts to theoriginating account that forward the platform message versusdisconnected users to the originating account that forward the platformmessage), time and date in which the platform message is transmitted,and other information. The message repository may include additional orother information about each platform message without departing from theinvention. The message repository may also include the topic associatedwith the platform message if the platform message was analyzed using themethod shown in FIG. 2 . Those skilled in the art will appreciate thatnot all platform messages will be associated with a topic.

In one embodiment of the invention, the message repository (108) trackswhich accounts interact with a given message and how the accountsinteract with a given message. The following describes non-limitingengagement examples: A social network platform user “Mary” authors themessage “I like Coffee Co.'s new flavors!” and submits it to the socialnetwork platform. Mark reads the message on the social network platform,and reposts the message using his own account in the form “Repost @Mary:‘I like Coffee Co.'s new flavors!’”. Mark has engaged with Mary'smessage. Alison also sees Mary's message, and composes a message inresponse taking the form “I do too! @Mary: ‘I like Coffee Co.'s newflavors!’”. Alison has engaged with Mary's message. Finally, John viewsMary's message and clicks an “Agree” button next the message asdisplayed on the social network platform website. John has engaged withthe message. In one or more embodiments of the invention, the engagementby Mark and Alison, and John is tracked using the engagement data items,which are stored in the message repository (or in another repositorythat is within operatively connected to the social network).

In one or more embodiments of the invention, each engagement data itemincludes a message identifier (an identifier that uniquely identifiesthe platform message within the message repository), an engaging accountidentifier, an engagement type, an engagement source, an engagementtimestamp, and an engagement location. Each of the aforementionedcomponents is described below except for message identifier which hasbeen previously described.

In one or more embodiments of the invention, the engaging accountidentifier identifies the account engaging with the message. In one ormore embodiments of the invention, the engagement type identifies thetype of engagement. The engagement type may be one of a set ofidentifiers that refer to a different kind of engagement. Engagementtypes may include, but are not limited to, “view” (or “view/expand”),“repost”, “reply”, and “agree”. In one embodiments of the invention eachengagement type is associated with an engagement weight, which may beused to convey the relative level of engagement between different typesof engagement types. For example, a “click-through” engagement type(e.g., when a user of an account clicks on a link included within theplatform message) may have a greater engagement weight than a“view/expand” engagement type (e.g., when an entity expands the visibleportion of the platform message such that the user of the account canview the entire message content). The engagement weight may be storedwithin the engagement data item, for example, as part of the engagementtype. Alternatively, the engagement weight for each engagement type maybe maintained in a separate location in the social network platform.

In one or more embodiments of the invention, the engagement sourceidentifies the client device type though which the account was engagingwith the platform message. Specifically, the engagement sourceidentifies the mechanism used by the engaging account to repost,distribute, respond, and otherwise engage with the platform message. Theengagement source may identify, for example, a website that interactswith the frontend of the social network platform, an application on amobile device, or a desktop application.

In one or more embodiments of the invention, the engagement timestamp isa record of the time the engagement occurred. In one or more embodimentsof the invention, the engagement timestamp includes the time zone inwhich the time was recorded. In one or more embodiments of theinvention, the engagement location is a record of the geographiclocation from which the account (via a client device) engaged with theplatform message. The engagement location may be global positioningsatellite coordinates of the client device submitted to the socialnetwork platform with the engagement (e.g., as recorded by the clientdevice at the time of the engagement).

In one or more embodiments of the invention, an engagement type of“view” (or view/expand”) indicates that the platform message was viewed.The platform message may be viewed on a client that is implementing aclient version of the social network platform (e.g., a mobile Twitterapplication executing on a smart phone). In another embodiment of theinvention, a “view” may also indicate that the platform message wasviewed on a website in which it was embedded. For example, a widget maybe embedded on a website where the widget shows platform messages. Thewidget may concurrently show multiple platform messages and the specificplatform message(s) that the widget displays may change over time. Whena platform message is presented via the widget and/or a user clicks onthe platform message within the widget, an engagement data item iscreated, where the engagement data item indicates an engagement type of“view” and also includes the universal resource locator (URL) and/or anyother data identifies the website on which the particular platformmessage was displayed.

In one or more embodiments of the invention, an engagement type of“repost” indicates that an account that received the platform messagesubsequently transmitted the message to other accounts in the socialnetwork platform. One example the platform message was viewed. Onenon-limiting example of a “repost” engagement type is a “retweet” of theplatform message.

In one or more embodiments of the invention, an engagement type of“reply” indicates that an account that received an initial platformmessage subsequently generates a new platform message in order to reply(or respond) to the content of the initial platform message. The newplatform message may be transmitted only to the author of the initialplatform message and/or may be transmitted to other accounts in thesocial network platform. The new platform message may include areference to the original platform message and/or a reference to theaccount (or author of the account) that authored the initial platformmessage.

In one or more embodiments of the invention, an engagement type of“agree” indicates that an account that received the platform messageagrees with the content of the platform message. The indication that agiven account agrees with the platform message may only be conveyed tothe author of the platform message and/or may be conveyed to otheraccounts in the social network platform.

The invention is not limited to the engagement data item describedabove.

Continuing with the discussion of FIG. 1 , the classification engine(110) stores one or more classification models for classifying (orcategorizing) a message selected from the message repository (108). Inone or more embodiments of the invention, the classification engine(110) may include an individual model for each modality (describedbelow) of a message. Alternatively, the classification engine (110) maybe a single, complex model configured to classify all the modalities ofa message together and output a result. Message modality refers toformat of the content of a message. For example, a text portion of amessage is one modality and an image portion of a message is anothermodality. Other modalities may also exist for the content of a message.In one or more embodiments of the invention, the engagement data itemsdescribed above that are associated with a message may also correspondto another modality that is distinct from the text and the imagemodalities of a message. Those skilled in the art will appreciate thatthe modalities of a message are not limited to the examples above.

Thus, a message associated with social network platform (102) may bemulti-modal. Accordingly, classification models may take into accountthe types of modality of a message and the timing associated with themodality. For example, an early fusion classification model may combinethe features (e.g., text, image, engagement data items, etc.) whichcorrespond to the different modalities of a message into one featurevector in order to determine an overall classification for the entiremessage. In contrast, a late fusion classification model may build andtune a classification model for each modality associated with a messageand combine the results of applying each individual model at a laterpoint in time. Those skilled in the art will appreciate that eachclassification model type may have its own set of advantages anddisadvantages. For example, in the late fusion models, there exists morefreedom to create the training and data sets for building individualmodels for each message modality. Further, with late fusion models,properties of the social network platform such as time-delayedengagement data items and the like may be leveraged to obtain a morecomprehensive classification. However, the output may be delayed. Withearly fusion classification models, there exists the possibility of areal-time (or near real-time) classification that may also be desired insome instances.

In one or more embodiments of the invention, the classification engine(110) includes decision logic for determining which classificationprevails when a multi-modal message results in conflictingclassifications for each modality (see e.g., FIG. 4 ). In one or moreembodiments of the invention, the aforementioned late fusionclassification models may use such decision logic for combining theindividually tuned classification models for each message modality.Further, the classification engine (110) is configured to receiveengagement data items and other data associated with a message for themessage is making such determinations regarding conflictingclassifications.

In one or more embodiments of the invention, one or more of theaforementioned repositories may be implemented as a storage serviceusing service-oriented architecture (SOA) and configured to receiverequests for data and to provide requested data to other components ofthe social network platform (102). In another example, one or more ofthe aforementioned repositories may include one or more tables in adistributed database management system (DBMS), a clustered database, astandalone flat file, and/or any storage software residing on one ormore physical storage devices. Examples of a storage device may include,but are not limited to, a hard disk drive, a solid state drive, and/orother memory device. Any type of database or storage application may beused, in accordance with various embodiments of the invention.

The invention is not limited to the system shown in FIG. 1 .

FIG. 2 shows a flowchart for classification of a message in accordancewith one or more embodiments of the invention. While the various stepsin these flowcharts are presented and described sequentially, one ofordinary skill will appreciate that some or all of the steps in the flowcharts may be executed in different orders, may be combined or omitted,and some or all of the steps may be executed in parallel. Further, themethods described in the following flowchart may be performed formultiple platform message in parallel or concurrently (e.g., there issome overlap in the processing of two or more platform messages) by thesocial network platform.

In step 200, a platform message is selected. In step 202, aclassification model stored in the classification engine is selectedbased on the modalities of the selected platform message. For example,if the message includes both text and image content, then adual-modality classification model (or a multi-modal classificationmodel) may be selected in step 202. Alternatively, multipleclassification models may be selected for each modality of the messageand then combined to obtain an overall classification.

In step 204, the message is processed through the selectedclassification model to obtain an initial message classification. In oneor more embodiments of the invention, an initial classification may be acategory or topic that is associated with the message or which describesthe content of the message. The initial message classification may be ofa single modality of the message, or may take into account all thedifferent modalities of the selected message. In one or more embodimentsof the invention, an initial classification may be “ignore” or “do notpost” or an equivalent label that results in that particular modalitybeing analyzed not being posted on the social network platform.

In step 206, a determination is made as to whether engagement datasurrounding the selected platform message is greater than a thresholdamount. That is, if the conversation around a particular platformmessage that has been initially classified by the classification engineis higher than a threshold amount, then the initial messageclassification is validated in step 208, to determine whether theinitial message classification is indeed the correct classification forthe message after observing the engagement around the message. In otherwords, the amount of engagement surrounding a message may change theinitial classification of the message. In one or more embodiments, thethreshold amount of engagement may be a predetermined number ofreposts/replies/agrees of the message. However, if the engagementsurrounding an initially classified message is not greater than athreshold amount (step 206), then the initial message classification ismade the final message classification of the selected platform messagein step 214. Those skilled in the art will appreciate that step 206 mayoccur after a certain time period has passed from the posting of theselected platform message in step 200. Because one of the inherentproperties of the social network platform is that engagement dataassociated with a message may occur after some delay, e.g., 24-48 hoursafter the original posting of the message on the social networkplatform, steps 206-214 may not occur in real-time.

When the engagement data items associated with the selected platformmessage are greater than the threshold, the initial messageclassification is validated in step 208. For example, 24-48 hours aftera message is posted on the social network platform, the message maybecome the center of social network conversation that may or may not berelated to the topic(s) of the initial classification of the message. Inthis case, the initial classification would need to be validated, toensure that the message is classified accurately after engagementassociated with the message is received by the social network platform.As another example, validation of a message may involve determining theinterested topics of accounts which reposted, replied to, or agreed withthe message. In one or more embodiments of the invention, validationensures that the message is classified using the right key words thatdescribe the content of the message.

In one or more embodiments of the invention, validation of a message mayinvolve selecting a classification model for the engagement data itemsassociated with the message, re-processing the message through thatclassification model, and comparing the initial classification modelresults with the validation classification model results. Alternatively,in one or more embodiments of the invention, validation of a message mayinvolve inputting the original classification model results into acomplex classification model capable of multiple modality inputs, andobtaining a new classification result based on the multi-modalclassification model. More specifically, validation of theclassification of a message may involve obtaining the engagement dataitems associated with the message and determining the topic(s) and/orinterest areas of the engagement data items.

Regardless of which classification model is chosen, a determination ismade in step 210 as to whether the classification of the message haschanged from the initial message classification. If the classificationof the message resulting from validation is different than the initialmessage classification, then the initial message classification isreplaced with the validated message classification in step 212. Thepost-validation message classification is then made the final messageclassification in step 214. Alternatively, if the classification thatresults from the validation process is the same as the initial messageclassification (i.e., if both classifications for the different messagemodalities agree), then no change is detected and the initialclassification is made the final classification of the message in step214.

In one or more embodiments of the invention, either the initial messageclassification obtained in step 204 or the final message classificationobtained in step 214 may be used in real-time to perform certainclassification related actions. For example, from either step 204 or214, the process may optionally directly proceed to step 216, in whichrecommendations based on the initial message classification are providedto the social network user, or step 218, where the message is added tothe message search pool. Referring to step 216, for example, as isdescribed in further detail below in FIG. 3 , if the initial messageclassification obtained in step 204 or the final message classificationobtained in step 214 is based on the text of the message, theinitial/final classification may be a selected topic (e.g., a category)which is associated with the message text content. This selected topicis then used to provide one or more recommendations. In one embodimentof the invention, providing a recommendation includes (i) identifyingaccounts that are associated with interest topics and/or expertisetopics associated with the topic identified in step 212 or 218 and (ii)providing the platform message, e.g., displaying the platform message,to one or more users of the accounts identified in (i) via one or moreclient devices, where none of the accounts identified in (i) arecurrently following the account that authored the platform message. Inthis context, when a first account “follows” a second account, the firstaccount may receive platform messages that are authored by the secondaccount, these platform messages may be direct messages (i.e., platformmessages only intended for the first account) or multicast messages(i.e., message intended for all accounts that are “following” the secondaccount).

In another embodiment of the invention, providing a recommendation mayinclude (i) identifying accounts that are associated with interesttopics and/or expertise topics associated with the topic identified asthe initial message classification in step 204 or the final messageclassification in step 214, and (ii) providing a notification to each ofthe accounts identified in (i) via one or more client devices, wherenone of the accounts identified in (i) are currently following theaccount that authored the platform message and the notificationrecommends that the identified accounts follow the account. For example,the notification (which may be in the form of a platform message) mayindicate that that account that received the notification should followthe account that authored the platform message (i.e., the platformmessage selected in step 200).

In another embodiment of the invention, the recommendations may includeadvertisements (which may also be native advertisements) where theadvertisements correspond to a product or service related to at leastthe topic identified in step 204 or 214. Once the appropriateadvertisements are determined, they are displayed to the account thatauthored the platform message via one or more client devices. Forexample, if the selected topic for a platform message resulting from theprocessing of the message through the selected classification model is“American Football” and the account that authored the platform messagedoes not include an interest of “American Football,” then the accountthat authored the platform message may thereafter be associated with aninterest of “American Football” and subsequently receive advertisementsfor products and/or services associated with American Football. Theadvertisements may be obtained from any known source include, but notlimited to, an advertising exchange operatively connected to the socialnetwork platform.

Alternatively, upon obtaining the initial message classification in step204 and/or the final message classification in step 214, which again maybe in the form of a selected topic associated with the text of themessage, the process may optionally directly proceed to step 218. Instep 218, the platform message is added to the message search pool. Inone embodiment of the invention, step 218 is performed after step 204regardless of whether step 216 is performed. In other embodiments of theinvention, step 218 is performed and step 216 is not performed. Finally,in other embodiments of the invention, step 216 is performed and step218 is not performed.

In one embodiment of the invention, the message search pool onlyincludes platform messages that have been associated with at least aninitial message classification (e.g., a selected topic, an imageclassification, etc.). Accordingly, the message search pool may onlyinclude a small subset of the total platform messages associated withthe social network platform. In one embodiment of the invention, onlythe platform messages in the message search pool may be used to servicesearch requests (or queries). Accordingly, in one embodiment of theinvention, if a platform message is not analyzed in accordance with FIG.2 and/or is analyzed in accordance with FIG. 2 but is ultimately notassociated with a topic then the platform message will not be present inany responses to search requests. Those skilled in the art willappreciate that while the below discussion of step 218 focuses ontopic(s) classifications associated with a platform message in step 204,the initial message classification may be an image classification oranother type of category associated with the message. Accordingly, inthe below discussion, the terms “topic(s)” and “classification(s)” areinterchangeable.

In one embodiment of the invention, when a search query (or searchrequest) is received by the social network platform, the social networkplatform performs the following steps: (i) analyze search request todetermine topic(s) associated with search request; (ii) query messagesearch pool in order to identify platform message associated withtopic(s) identified in (i); and (iii) return all of (or a subset of) (ifpresent in the message search pool) platform message that are associatedwith the topic(s) identified in (i). The platform messages returned in(iii) may be display on the client device that issued the searchrequest. The platform messages returned in (iii) may be displayed in anyformat and/or in any representation without departing from theinvention.

In one embodiment of the invention, the search query may be used toidentify the top trending topics in the social network. In such ascenario, the message search pool may be queried, for example, todetermine the top five trending topics. The topic five tropics may beascertained by determine the number of platform messages in the messagesearch pool associated with each topic and then ranking the topics basedon the number of associated platform messages to obtain a list of rankedtopics. Finally, the top five ranked topics from the list of rankedtopics may then be provided in response search query.

FIG. 3 shows a flowchart for topic disambiguation in accordance with oneor more embodiments of the invention. Specifically, FIG. 3 shows anexample of a process for classifying the text portion of a message. Inother words, FIG. 3 shows the process for classifying one particularmodality of a message: text. In one or more embodiments of theinvention, when a message includes only text or is a multi-modal messagethat includes text, the message may be classified by associating themessage with a topic, as described in FIG. 3 below. While the varioussteps in these flowcharts are presented and described sequentially, oneof ordinary skill will appreciate that some or all of the steps in theflow charts may be executed in different orders, may be combined oromitted, and some or all of the steps may be executed in parallel.Further, the methods described in the following flowchart may beperformed for multiple platform message in parallel or concurrently(e.g., there is some overlap in the processing of two or more platformmessages) by the social network platform.

In step 300, a platform message is selected. In step 302, engagementinformation (if available) is obtained for the selected platformmessage. In one or more embodiments of the invention, the engagementdata includes one or more engagement data items.

In step 304, a determination is made about whether the engagement levelfor the platform message within the social network is above an analysisthreshold. This is similar to step 206 as discussed above. For example,if the platform message is associated with N engagement data items,where N is greater than or equal to a number corresponding to theanalysis threshold, then the engagement level for the platform messagemay be greater than or equal to the analysis threshold. In anotherembodiment, the analysis threshold may be defined in relative terms (asopposed to in absolute terms). For example, the analysis threshold mayspecify that the top 1000 platform messages, ranked based on level ofengagement, transmitted within the social network over a certain periodof time (e.g., within the last hour) meet or exceed the analysisthreshold. Other mechanisms for determining whether a given platformmessage should be analyzed using the method shown in FIG. 3 may be usedwithout departing form the invention.

In one embodiment of the invention, all platform messages within thesocial network are analyzed and, as such, steps 302 and 304 are notperformed. In other embodiments of the invention, the steps 302 and 304are performed in order to identify a subset of platform messages thatshould be analyzed using the method shown in FIG. 3 . In such scenarios,there are a significant number of platform messages being transmittedwithin the social network platform and, for example, due to resourceconstraints and/or latency constraints, only a subset of platformmessages may be processed.

In one embodiment of the invention, if no engagement information isavailable for a given platform message then either (i) the processing ofthe platform message may cease following step 304 or (ii) the processingof the platform message may proceed from step 302 directly to step 306.

In one embodiment of the invention, step 304 is performed for allplatform messages which include text, and only the messages that exceedan analysis threshold (as described above) are selected in step 300 forprocessing. In such cases, the method shown in FIG. 3 does not includesteps 302 and 304.

Continuing with the discussion of FIG. 3 , in step 306, the content ofthe platform message is obtained and processed using semantic analysisin order to determine a set of possible topics with which the platformmessage is associated. That is, because the modality of message beinganalyzed in FIG. 3 is text, semantic analysis may be chosen as theclassification model for the message. The resulting set, denoted as{topics} may include zero, one or more topics. Further, each topic maybe associated with a topic confidence rating, which indicates theprobability (or likelihood) that a platform message is associated with agiven topic. For example, if the message content is “Great tackle bySimon”, then the potential topics may be (i) Football—60% and (ii)Rugby—30%. Accordingly, in this example, there is 60% chance that theplatform message is about football and the 30% chance that the messageis about rugby.

The semantic analysis may use one or more topic models from the topicmodel repository. The topic models may include keywords, phase, etc. andcorresponding topics with which they may be associated. The topic modelsmay also be used to ascertain the topic confidence rating associatedwith each topic. Any type of semantic analysis may be used withoutdeparting from the invention.

In one embodiment of the invention, if no possible topics are obtainedin step 306 then the process may end or may proceed directly to step314. In one embodiment of the invention, if there low level ofengagement for the platform message then the process may end.Alternatively, if there is a high level of engagement, then the processmay proceed even though the no topics were identified in Step 306.

In step 308, if two or more topics were identified in step 306, then adetermination is made about whether the topics conflict. In one or moreembodiments of the invention, topics may be considered to be conflictingif the topics are considered to be mutually exclusive and/or if there isa low likelihood than a single platform message may be possiblyassociated with the two or more topics. Step 308 may be performed usinginformation obtained from one or more topic repository models. Forexample, if the topics resulting from step 306 are “heavy metal” and“classical music” and one or more topic models in the topic modelsrepository indicates that these two topics are conflict, then a topicconflict is detected. If a conflict is detected, then the process ends;otherwise, the process proceeds to step 310. If there is only one topicobtained from step 306, then the process may proceed to step 314 andstep 308 may not be performed.

In step 310, a determination is made about whether any of the topicsidentified in step 306 are associated with a topic confidence ratingthat is greater than or equal to the topic confidence threshold. Thetopic confidence threshold corresponds to a threshold at (or abovewhich) the platform message is deemed to be associated with a giventopic. For example, if the potential topics for are given platformmessage are (i) Football—60% and (ii) Rugby— 30% and the topicconfidence threshold is 75%. Then none of the topics satisfy the topicconfidence threshold and, accordingly, the process proceeds to step 314.Alternatively, if the topic confidence threshold is 58%, then theplatform message would be deemed to be associated with the topic of“football” and the process would proceed to step 312.

In step 312, the topic that is associated with a topic confidence ratingthat is greater than or equal to the topic confidence threshold isselected as the topic that is associated with the platform message. Thistopic may become the initial classification of the message. The processthen ends.

In step 314, the interest topics and/or expertise topics (if available)for the account that authored the platform message are obtained. Forexample, the account repository may be queried to determine whether theaccount that authored the platform message is associated with anyinterest topics and/or expertise topics. For each interest topic and/orexpertise topic, the account repository may include an interest leveland/or an expertise level. The interest level indicates (e.g., on arelative basis) how interested the account that authored the platformmessage is about a given topic. For example, the interest topics may befashion—10%; gardening—80%, which indicates that the account is muchmore interested in gardening than in fashion. The expertise levelindicates (e.g., on a relative basis) how much of an expert othersconsider the account that authored the platform message to be about agiven topic. For example, the expertise topics may be Football—60%;rugby—30%, which indicates that the account is considered to be more ofan authority on football than on rugby. The process may proceed directlyfrom step 314 to step 318; alternatively, the process may proceed fromstep 314 to step 316.

In step 316, the interest topics and/or expertise topics (if available)for accounts that engaged with the platform message are obtained. Forexample, using the engagement data items associated with a givenplatform message, one or more accounts that engaged with the platformmessage may be identified. The identity of the accounts may then be usedto lookup account information in the account repository, where theaccount information may include zero, one or more interest topics peraccount and zero, one or more expertise topics per account.

In step 318, the information about potential topics obtained in step 306along with additional information (e.g., information obtained in steps314 and/or 316) is used to perform topic disambiguation in order todetermine the topic that is associated with the platform message. Thetopic disambiguation may use the aforementioned information to determinethe likelihood that a given topic is associated with the platformmessage. The topic with the highest likelihood of being associated withthe platform message may be used to select the topic associated with theplatform message. This topic becomes the initial classification of themessage in step 312.

In one embodiment of the invention, the information obtained in step 316is used to determine the distribution of interest topics and/orexpertise topics of accounts that are engaging with the message.Further, those skilled in the art will appreciate that the end of theprocess of FIG. 3 results in an initial classification, which may stillbe validated. That is, FIG. 3 ends at step 204 in FIG. 2 , and theprocess continues from there to validate and determine a finalclassification for the text content of the selected message. In one ormore embodiments of the invention, after topic disambiguation, theprocess may proceed to classify other modalities of the same selectedmessage (e.g., to classify image content of the message as describedbelow in FIG. 4 ), to validate the initial message classificationobtained in FIG. 3 , or a combination of both. Further, in accordancewith FIG. 2 , in one or more embodiments of the invention, the initialclassification obtained at the conclusion of FIG. 3 may directly be usedto provide recommendations and/or may be used to save the initiallyclassified message in the message search pool.

The following non-limiting examples illustrate various embodiments oftopic disambiguation performed in step 318 to obtain an initialclassification for a message containing text.

Example 1

Consider a scenario in which the following platform message is selectedin step 200: “That was a great tackle by Smith”. When the platformmessage is analyzed in accordance with step 306, the following potentialtopics are identified: American football—topic confidence rating of 60%;soccer—topic confidence rating of 40%; rugby—topic confidence rating of40%. Further, in accordance with the performance of step 314, it isdetermined the account that authored the platform message has thefollowing topics of expertise: American Football—expertise level: 60%;soccer—expertise level—0%; rugby—expertise level of 10%. Theaforementioned information is taken into account in Step 218 in order todetermine the following: 36% (60%*60%) likelihood of the topic beingAmerican Football, 0% likelihood of the topic being soccer; and 4%(40%*10%) likelihood of the topic being rugby. In view of the aboveresults, a topic of American Football is associated with the platformmessage. This information may be stored with the platform message in themessage repository (see FIG. 1 ).

Example 2

Consider a scenario in which the following platform message is selectedin step 300: “That was a great tackle by Smith”. When the platformmessage is analyzed in accordance with step 306, the following potentialtopics are identified: American football—topic confidence rating of 60%;soccer—topic confidence rating of 40%; rugby—topic confidence rating of40%. Further, in accordance with the performance of step 316, it isdetermined the account that authored the platform message has no knownexpertise topics. However, in accordance with step 316 it is determinedthat the accounts that engaged with the platform message have thefollowing interest distribution: American Football— 30% of engagingaccounts; soccer 50% of engaging accounts, 20% of the account hasinterests other than American Football and soccer. The aforementionedinformation is taken into account in Step 318 in order to determine thefollowing: 18% (60%*30%) likelihood of the topic being AmericanFootball, 20% (40%*50%) likelihood of the topic being soccer; and 0%likelihood of the topic being rugby. In view of the above results, atopic of soccer is associated with the platform message. Thisinformation may be stored with the platform message in the messagerepository (see FIG. 1 ).

Example 3

Consider a scenario in which the following platform message is selectedin step 300: “That was a great tackle by Smith”. When the platformmessage is analyzed in accordance with step 206, the following potentialtopics are identified: American football—topic confidence rating of 60%;soccer—topic confidence rating of 40%; rugby—topic confidence rating of40%. Further, in accordance with the performance of step 314, it isdetermined the account that authored the platform message has thefollowing topics of expertise: American Football—expertise level: 60%;soccer—expertise level—30%; rugby—expertise level of 10%. Finally, inaccordance with step 216 it is determined that the accounts that engagedwith the platform message have the following interest distribution:American Football— 30% of engaging accounts; soccer—50% of engagingaccounts, rugby 20% of engaging accounts. The aforementioned informationis taken into account in step 318 in order to determine the following:11% (60%*60%*30%) likelihood of the topic being American Football, 6%(40%*30%*50%) likelihood of the topic being soccer; and 1% (40%*10%*20%)likelihood of the topic being rugby. In view of the above results, atopic of American Football is associated with the platform message. Thisinformation may be stored with the platform message in the messagerepository (see FIG. 1 ).

Those skilled in the art will appreciate that (i) other mathematicalfunctions may be used to combine the information obtained in steps 306,314 and 316 without departing from the invention and (ii) that step 318may take into account other information (i.e., information other thaninformation that is obtained in steps 306, 314, and 316) withoutdeparting from the invention.

FIG. 4 shows a flowchart for classifying a platform message whichincludes image data in accordance with one or more embodiments of theinvention. Specifically, FIG. 4 shows the process by which a messagehaving only image data or image data in combination with othermodalities, for example, text data or engagement data items, isclassified. While the various steps in these flowcharts are presentedand described sequentially, one of ordinary skill will appreciate thatsome or all of the steps in the flow charts may be executed in differentorders, may be combined or omitted, and some or all of the steps may beexecuted in parallel. Further, the methods described in the followingflowchart may be performed for multiple platform message in parallel orconcurrently (e.g., there is some overlap in the processing of two ormore platform messages) by the social network platform.

Initially, in step 400, a platform message is selected. While FIG. 4describes a standalone method for classifying image content of anyplatform message, in one or more embodiments of the invention, theplatform message selected in step 400 may be the same message selectedin step 300 of FIG. 3 , when that message has both text and imagecontent, for example. In step 402, the message is determined to includean image. The message may include only an image, or may be a multi-modalmessage having an image content and other types of modalities.

In step 404, raw pixel analysis of the image is performed to determinethe image content. Those skilled in the art will appreciate that the rawpixel analysis may be done using any suitable method well-known in theart. At this stage, a determination is made as to whether the imagecontent meets a predetermined classification criteria (step 406).Specifically, the social network platform may store predeterminedcriteria that establishes whether an image is not suitable for postingon the social network platform. For example, if the image containsweapons, this may indicate a violent image that is not suitable forpublic access. As another example, if the image is found to include toomuch bare human skin, this may be considered inappropriate content.Thus, in step 406, a filter is applied to ensure that the content of theanalyzed image passes the predetermined criteria for public access tothe image.

In one or more embodiments of the invention, when the image does notmeet the predetermined classification criteria, a second determinationis made as to whether the image is multi-modal in step 408. If the imageis multi-modal, the other modalities of the message (e.g., text,engagement data items, etc.) may be used to classify the message andobtain an initial classification of the message in step 410. In thiscase, the image content may not be classified at all. Alternatively,classification of the image may occur when engagement data surroundingthe image is generated after some period of time, and the image contentis validated to determine whether the initial ‘no classification’decision was properly determined.

Those skilled in the art will appreciate that when the other modalitiesof the message are used to classify the message, and when an initialmessage classification is obtained using only the other modalities, anyrecommendations provided based on the initial message classificationwould be based only on the modality(ies) used to actually classify themessage. Thus, for example, if the image content of a message is ignoredby a classification model, then the recommendations or advertisementsprovided by the social network platform based on classification of themessage may be restricted to the text or engagement data of the message.Similarly, in one or more embodiments of the invention, only theclassified portion of the message may be added to the message searchpool, whereas the unclassified image content, for example, of themessage is not included as part of the message that is searchable. Inaddition, when the process of FIG. 2 is complete, and a final messageclassification is obtained, this may result in a different portion orthe message in its entirety to be used for providing recommendations andfor storing in the message search pool.

In step 406, when the image meets the predetermined classificationcriteria, the image content of the message is classified in step 412.Classification of the image may be performed as described in any of theembodiments above with respect to steps 202-204 of FIG. 2 . In step 414,upon classification of the image content of the message, a determinationis made as to whether the message is multi-modal. If the message ismulti-modal, this indicates that further classification of the messageis required. Accordingly, the other modalities of the message areclassified. The classification of the other modes may take the form ofFIG. 3 , for example, if the message also contains text. During or uponobtaining an initial classification of the other modalities, in step416, a determination is made as to whether the same classification isobtained for all of the different modalities of the message. If theclassification of all of the message modalities agree (i.e., theclassifications are not determined to conflict with each other), then acombined initial classification of the message is obtained in step 418and the process ends.

In one or more embodiments, if the classifications of the messagemodalities do not agree in step 416 (i.e., there is a conflict), adecision rule may be applied to determine the initial classification ofthe message in step 420. The decision rule in step 420 may take intoaccount the confidence ratings of the classifications, the interestand/or expertise information of the author of the message beinganalyzed, engagement data associated with the message, or any othersuitable information. In one or more embodiments of the invention, thedecision rules may be stored in the classification engine described inFIG. 1 . Alternate locations for storing decision logic may also exist.Further, application of the decision rule may be implemented asdescribed in steps 308-312 and/or 314-318 of FIG. 3 .

The following non-limiting examples illustrate various embodiments forperforming image classification in accordance with the process describedin FIG. 4 .

Example 4

Consider a scenario in which a platform message selected in step 400includes only an image of a lingerie model on a runway. In this case,when the image is analyzed in step 404, the image content in step 406may not meet the predetermined criteria for classification. That is,because the raw pixel data of the image results in a determination thatthe bare skin content of the image is high, the image may be determinedby the platform as offensive, inappropriate, or not safe for viewing inany setting (e.g., not safe for work, etc.). Accordingly, the processwould end after determining that the message is not multi-modal in step408. In this case, the initial classification of the message may be thatthat the message content is “Do Not Post”, that the image content isignored, or some suitable label that indicates that the message is notsuitable for posting on the social network platform.

Now suppose that 24-48 hours later, the image is re-posted on the socialnetworking platform with text about a fashion show, and many people fromthe fashion community have agreed with or re-posted the image. In thiscase, when the engagement data surrounding the message is analyzed instep 206, the engagement data is greater than the threshold and themessage classification proceeds to validation of the initialclassification. Upon semantically analyzing the text accompanying theimage, obtaining interest and expertise information of the author of theplatform message, and analyzing the accounts engaging with the platformmessage including the image, topic disambiguation is performed using theobtained information and returns a result of topics {fashion, fashionshow}. In this scenario, the validation of the initial classificationresults in a change to the initial classification, from inappropriatecontent to an image about fashion, and the image is classified using thekey words fashion and fashion show. The image may then be placed in themessage search pool under these key words, and/or recommendations basedon the classification of the image may be provided.

Example 5

As another example, consider the scenario in which a multi-modal messageis selected. In this case, suppose the message includes the text: “Atthe Victoria's secret fashion show!” and also includes an image of afashion model modeling lingerie on the runway. In this example,classification of the image may result in an initial classification of“do not post” or “ignore;” however, when the multi-modal message isclassified including the text, the message may be classified as{fashion, fashion show}.

Now suppose the message goes viral, and conversation surrounding themessage becomes an event on the social network platform. Thisinformation may be delayed, and not in real-time, due to the inherentproperties of the platform. However, in this case, in one or moreembodiments of the invention, the engagement data surrounding themessage may also be classified with the same key words/topics {fashion,fashion show}, enabling the social network platform to classify entireconversations with the same searchable key words or topics.

While the above discussion indicates that the social network platformperforms the method shown in FIGS. 2-4 , one or more steps may beperformed by an entity (entities) (e.g., a service) executing externalto the social network platform. In such scenarios, the social networkplatform provides the information that is necessary to perform the oneor more steps to the entity and then receives the correspond resultsfrom the entity that performed by the one or more steps.

Embodiments of the invention may be implemented on virtually any type ofcomputing system regardless of the platform being used. For example, thecomputing system may be one or more mobile devices (e.g., laptopcomputer, smart phone, personal digital assistant, tablet computer, orother mobile device), desktop computers, servers, blades in a serverchassis, or any other type of computing system or devices that includesat least the minimum processing power, memory, and input and outputdevice(s) to perform one or more embodiments of the invention. Forexample, as shown in FIG. 5 , the computing system (500) may include oneor more computer processor(s) (502), associated memory (504) (e.g.,random access memory (RAM), cache memory, flash memory, etc.), one ormore storage device(s) (506) (e.g., a hard disk, an optical drive suchas a compact disk (CD) drive or digital versatile disk (DVD) drive, aflash memory stick, etc.), and numerous other elements andfunctionalities. The computer processor(s) (502) may be an integratedcircuit for processing instructions. For example, the computerprocessor(s) may be one or more cores, or micro-cores of a processor.The computing system (500) may also include one or more input device(s)(510), such as a touchscreen, keyboard, mouse, microphone, touchpad,electronic pen, or any other type of input device. Further, thecomputing system (500) may include one or more output device(s) (508),such as a screen (e.g., a liquid crystal display (LCD), a plasmadisplay, touchscreen, cathode ray tube (CRT) monitor, projector, orother display device), a printer, external storage, or any other outputdevice. One or more of the output device(s) may be the same or differentfrom the input device(s). The computing system (500) may be connected toa network (512) (e.g., a local area network (LAN), a wide area network(WAN) such as the Internet, mobile network, or any other type ofnetwork) via a network interface connection (not shown). The input andoutput device(s) may be locally or remotely (e.g., via the network(512)) connected to the computer processor(s) (502), memory (504), andstorage device(s) (506). Many different types of computing systemsexist, and the aforementioned input and output device(s) may take otherforms.

Software instructions in the form of computer readable program code toperform embodiments of the invention may be stored, in whole or in part,temporarily or permanently, on a non-transitory computer readable mediumsuch as a CD, DVD, storage device, a diskette, a tape, flash memory,physical memory, or any other computer readable storage medium.Specifically, the software instructions may correspond to computerreadable program code that when executed by a processor(s), isconfigured to perform embodiments of the invention.

Further, one or more elements of the aforementioned computing system(500) may be located at a remote location and connected to the otherelements over a network (512). Further, embodiments of the invention maybe implemented on a distributed system having a plurality of nodes,where each portion of the invention may be located on a different nodewithin the distributed system. In one embodiment of the invention, thenode corresponds to a distinct computing system. Alternatively, the nodemay correspond to a computer processor with associated physical memory.The node may alternatively correspond to a computer processor ormicro-core of a computer processor with shared memory and/or resources.

While the invention has been described with respect to a limited numberof embodiments, those skilled in the art, having benefit of thisdisclosure, will appreciate that other embodiments can be devised whichdo not depart from the scope of the invention as disclosed herein.Accordingly, the scope of the invention should be limited only by theattached claims.

What is claimed is:
 1. A system comprising one or more computers and oneor more storage devices on which are stored instructions that areoperable, when executed by the one or more computers, to cause the oneor more computers to perform operations comprising: obtaining, for amessage that was broadcasted on a social network platform and isassociated with an initial classification that identifies a first set oftopics for the message, topic engagement information about third partyaccounts engaging with the message, the topic engagement informationcomprising one or more distributions of topic interest scores, topicexpertise scores, or both, associated with the third party accounts;determining that the message is associated with an engagement-basedclassification using the topic engagement information, wherein theengagement-based classification associates the message with a second setof topics of a plurality of topics that includes the first set oftopics; validating the initial classification for the message by:determining whether the engagement-based classification is the same asthe initial classification; and determining a final classification forthe message using a result of the determination whether theengagement-based classification is the same as the initialclassification; generating, using the final classification, arecommendation for one or more other accounts of the social networkplatform; and broadcasting the recommendation to the one or more otheraccounts.
 2. The system of claim 1, wherein determining the finalclassification for the message comprises: determining that theengagement-based classification is the same as the initialclassification; and maintaining, as the final classification, theinitial classification in response to determining that theengagement-based classification is the same as the initialclassification.
 3. The system of claim 1, wherein determining the finalclassification for the message comprises: determining that theengagement-based classification is different from the initialclassification; and switching the initial classification for the messageto the engagement-based classification as the final classification, inresponse to determining that the engagement-based classification isdifferent from the initial classification.
 4. The system of claim 1,wherein determining that the message is associated with theengagement-based classification comprises: generating theengagement-based classification for the message using the topicengagement information, a content-based classification, and author topicscores.
 5. The system of claim 1, wherein the operations furthercomprise: determining, using the topic engagement information, whetheran engagement threshold is satisfied, wherein determining that themessage is associated with the engagement-based classification using thetopic engagement information is responsive to determining that theengagement threshold is satisfied.
 6. The system of claim 1, wherein:the final classification includes one or more topics from the pluralityof topics; and broadcasting the recommendation to the one or more otheraccounts comprises: identifying the one or more other accounts that areassociated with the one or more topics included in the finalclassification, wherein the one or more other accounts are not currentlyfollowing an account that authored the message; and broadcasting, to oneor more client devices each for one of the one or more other accounts,instructions to cause the one or more client devices to present themessage.
 7. The system of claim 1, wherein the operations furthercomprise: placing the message and data for one or more topics includedin the final classification of the message in a message search pool inthe social network platform, wherein the message search pool comprisesmessages broadcasted on the social network platform that are eachassociated with at least one of the plurality of topics usingcorresponding final classifications for the messages.
 8. The system ofclaim 7, wherein the operations further comprise: receiving a searchrequest for messages related to a particular topic; and responding,using topics for final classifications for one or more messages, to thesearch request with the one or more messages associated with theparticular topic in the message search pool.
 9. One or morenon-transitory computer storage media encoded with computer programinstructions that, when executed by one or more computers, cause the oneor more computers to perform operations comprising: obtaining, for amessage that was broadcasted on a social network platform and isassociated with an initial classification that identifies a first set oftopics for the message, topic engagement information about third partyaccounts engaging with the message, the topic engagement informationcomprising one or more distributions of topic interest scores, topicexpertise scores, or both, associated with the third party accounts;determining that the message is associated with an engagement-basedclassification using the topic engagement information, wherein theengagement-based classification associates the message with a second setof topics of a plurality of topics that includes the first set oftopics; validating the initial classification for the message by:determining whether the engagement-based classification is the same asthe initial classification; and determining a final classification forthe message using a result of the determination whether theengagement-based classification is the same as the initialclassification; generating, using the final classification, arecommendation for one or more other accounts of the social networkplatform; and broadcasting the recommendation to the one or more otheraccounts.
 10. The one or more non-transitory computer storage media ofclaim 9, wherein determining the final classification for the messagecomprises: determining that the engagement-based classification is thesame as the initial classification; and maintaining, as the finalclassification, the initial classification in response to determiningthat the engagement-based classification is the same as the initialclassification.
 11. The one or more non-transitory computer storagemedia of claim 9, wherein determining the final classification for themessage comprises: determining that the engagement-based classificationis different from the initial classification; and switching the initialclassification for the message to the engagement-based classification asthe final classification, in response to determining that theengagement-based classification is different from the initialclassification.
 12. The one or more non-transitory computer storagemedia of claim 9, wherein determining that the message is associatedwith the engagement-based classification comprises: generating theengagement-based classification for the message using the topicengagement information, a content-based classification, and author topicscores.
 13. The one or more non-transitory computer storage media ofclaim 9, wherein the operations further comprise: determining, using thetopic engagement information, whether an engagement threshold issatisfied, wherein determining that the message is associated with theengagement-based classification using the topic engagement informationis responsive to determining that the engagement threshold is satisfied.14. The one or more non-transitory computer storage media of claim 9,wherein: the final classification includes one or more topics from theplurality of topics; and broadcasting the recommendation to the one ormore other accounts comprises: identifying the one or more otheraccounts that are associated with the one or more topics included in thefinal classification, wherein the one or more other accounts are notcurrently following an account that authored the message; andbroadcasting, to one or more client devices each for one of the one ormore other accounts, instructions to cause the one or more clientdevices to present the message.
 15. The one or more non-transitorycomputer storage media of claim 9, wherein the operations furthercomprise: placing the message and data for one or more topics includedin the final classification of the message in a message search pool inthe social network platform, wherein the message search pool comprisesmessages broadcasted on the social network platform that are eachassociated with at least one of the plurality of topics usingcorresponding final classifications for the messages.
 16. The one ormore non-transitory computer storage media of claim 15, wherein theoperations further comprise: receiving a search request for messagesrelated to a particular topic; and responding, using topics for finalclassifications for one or more messages, to the search request with theone or more messages associated with the particular topic in the messagesearch pool.
 17. A computer-implemented method comprising: obtaining,for a message that was broadcasted on a social network platform and isassociated with an initial classification that identifies a first set oftopics for the message, topic engagement information about third partyaccounts engaging with the message, the topic engagement informationcomprising one or more distributions of topic interest scores, topicexpertise scores, or both, associated with the third party accounts;determining that the message is associated with an engagement-basedclassification using the topic engagement information, wherein theengagement-based classification associates the message with a second setof topics of a plurality of topics that includes the first set oftopics; validating the initial classification for the message by:determining whether the engagement-based classification is the same asthe initial classification; and determining a final classification forthe message using a result of the determination whether theengagement-based classification is the same as the initialclassification; generating, using the final classification, arecommendation for one or more other accounts of the social networkplatform; and broadcasting the recommendation to the one or more otheraccounts.
 18. The computer-implemented method of claim 17, whereindetermining the final classification for the message comprises:determining that the engagement-based classification is the same as theinitial classification; and maintaining, as the final classification,the initial classification in response to determining that theengagement-based classification is the same as the initialclassification.
 19. The computer-implemented method of claim 17, whereindetermining the final classification for the message comprises:determining that the engagement-based classification is different fromthe initial classification; and switching the initial classification forthe message to the engagement-based classification as the finalclassification, in response to determining that the engagement-basedclassification is different from the initial classification.
 20. Thecomputer-implemented method of claim 17, wherein determining that themessage is associated with the engagement-based classificationcomprises: generating the engagement-based classification for themessage using the topic engagement information, a content-basedclassification, and author topic scores.
 21. The computer-implementedmethod of claim 17, further comprising: determining, using the topicengagement information, whether an engagement threshold is satisfied,wherein determining that the message is associated with theengagement-based classification using the topic engagement informationis responsive to determining that the engagement threshold is satisfied.22. The computer-implemented method of claim 17, wherein: the finalclassification includes one or more topics from the plurality of topics;and broadcasting the recommendation to the one or more other accountscomprises: identifying the one or more other accounts that areassociated with the one or more topics included in the finalclassification, wherein the one or more other accounts are not currentlyfollowing an account that authored the message; and broadcasting, to oneor more client devices each for one of the one or more other accounts,instructions to cause the one or more client devices to present themessage.
 23. The computer-implemented method of claim 17, furthercomprising: placing the message and data for one or more topics includedin the final classification of the message in a message search pool inthe social network platform, wherein the message search pool comprisesmessages broadcasted on the social network platform that are eachassociated with at least one of the plurality of topics usingcorresponding final classifications for the messages.
 24. Thecomputer-implemented method of claim 23, further comprising: receiving asearch request for messages related to a particular topic; andresponding, using topics for final classifications for one or moremessages, to the search request with the one or more messages associatedwith the particular topic in the message search pool.